Almuayqil Saleh, Abd El-Ghany Sameh, Shehab Abdulaziz
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Department of Information Systems, Mansoura University, Mansoura 35516, Egypt.
Diagnostics (Basel). 2023 Mar 28;13(7):1268. doi: 10.3390/diagnostics13071268.
In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time.
面对新冠疫情，人们开展了许多研究，旨在为患者提供辅助建议，以帮助他们克服预计临床医生短缺所带来的负担。因此，本研究聚焦于使用一组经过微调的深度学习模型来诊断新冠病毒，以克服病毒检查中的延迟问题。利用五种近期的深度学习算法（EfficientB0、VGG-19、DenseNet121、EfficientB7和MobileNetV2），将CT扫描图像和胸部X光图像标记为新冠病毒阳性或阴性。实验结果表明，与现有最先进方法相比，该方法在精度、灵敏度、特异性、F1分数、准确率和数据访问时间方面具有优势。
